The electrical impedance tomography takes advantage of the differing specific conductivity of human tissues, which varies from 15.4 mS/cm for cerebrospinal fluid to 0.06 mS/cm for bone. The difference in the value of conductivity is large between different tissues. Cross sectional images of the distribution of conductivity or alternatively specific resistance therefore show a good contrast. The aim of electrical impedance tomography is to produce images of those contrasts.
An example of carrying out an EIT measurement is the analysis of a patient's lang. A number of electrodes are placed around the thorax, wherein an alternating current with e.g. 50 kHz at 5 nA peak to peak amplitude is applied to respectively adjacent electrodes. The other electrodes respectively are used with the alternating current to carry out the measurement of impedance against a defined reference potential. As soon as all the electrodes, one after another, have served as current conducting electrodes, a cycle for data detection is concluded in order to eliminate statistical disturbances, as a rule a plurality of data detection cycles is averaged, in order to obtain a corresponding picture.
The maximum impedance changes in the zone of the thorax are caused by breathing in and out of air. In this context it can be observed that the impedance change which is measured by electrodes is a measure of the change of volume in the lung. Therefore, according to the process of EIT, measurements can also be carried out with respect to the pressure-volume relationship of the lung.
The complete reconstruction problem is non-linear and requires iteration. However, each step in the iterative process is linear. Images reconstructed using only the first step of iteration effectively treat image formation as a linear process, an assumption approximately justified for small changes in conductivity from uniform. Most of the clinical images produced today are using a single-step reconstruction algorithm.
One aim of EIT is to reconstruct images of the absolute distribution of conductivity. These images are known as absolute images. However, this requires that the forward problem can be solved to a high degree of accuracy, and this can be difficult. The magnitude of the voltage signal measured on an electrode or between electrodes will depend on the body shape, the electrode shape and position, and the internal conductivity distribution. The signal magnitude is in fact dominated by the first two effects rather than by conductivity. However, it a change in conductivity occurs within the object, than it can often be assumed that the change in surface voltage is dominated by this conductivity change. In differential imaging, the aim is to image changes in conductivity rather than absolute values.
Differential algorithms can only image changes in conductivity. Absolute distributions of conductivity cannot be produced using these methods. In addition, any cross movement of the electrodes, either because they have to be removed and replaced or even because of significant patient movement, make the use of this technique difficult for long-term measurement of changes. As an alternative to changes in time, differential algorithms can images changes in conductivity with frequency, Measurements can be made over a range of frequencies and differential images can be produced using data from the lowest frequency and the other frequencies in term. A multi-frequency measurement thereby makes use of the complex resistance of a tissue which depends on the frequency.
As it becomes obvious, the analysis of a patient's lung by electrical impedance tomography yields a vast amount of data. An BIT image consists of a plurality of pixels, wherein each pixel can be determined by different reconstruction techniques as described above, i.e. by the determination of the absolute distribution, the relative distribution or distribution over a range of frequencies.
On the other hand, there are also a plurality of lung conditions which have to be determined from the plurality of EIT data. Basically, this derives from the fact that in the lung there are theoretically four types of alveoli, which is shown in FIG. 1. The normal alveolus (A) is both ventilated and perfused with blood, There are alveoli that are ventilated, but not perfused (B); such alveoli contribute significantly to the physiologic dead space. There are alveoli that are not ventilated, but perfused (C); such alveoli do not provide the exchange of respiratory gases. Finally, there are alveoli that are both poorly ventilated and poorly perfused (D); such alveoli contain high CO2 and N2 and O2. These alveoli are the last to expel their CO2 and N2 in washout tests.
An experienced doctor is able to use the plurality of EIT data in order to determine the plurality of different lung conditions. This is conventionally done by analysing the different types of reconstruction images according to the absolute distribution, the relative distribution and the distribution over a certain range of frequency. However, even for an experienced doctor it is still very time-consuming using a conventional EIT monitor to come to sufficient results.